Name: Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Vi takker Wuhan Urban Construction Group 2023 Annual Scientific Research Project (NO.7) for at finansiere dette arbejde.

1. Konkret prøvebillede erhvervelse
<ol><li>Transport beton til arbejdspladsen, hvor den hældes af pumpebilen.</li>
	<li>For at tage billeder skal du tænde for optageudstyret ved at flytte tænd / sluk-tastkontakten til højre og dreje den til ON-position . Juster kameraets tilstandsknap til den grønne automatiske tilstand, og sørg for, at kameralinsen er parallel med betonoverfladen, og tryk på udløsertasten. Tag 20 billedprøver af ikke-vibreret beton, og gem ...

Directional Gradient Histogram and SVM for Identifying Concrete Vibration States

<ol>
	<li>Jiang, L., Tian, Z., Wang, K., Sun, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimating+the+segregation+of+concrete+under+vibration+based+on+electrical+method.">Estimating the segregation of concrete under vibration based on electrical method.</a> Concrete. 1, 41-44 (2023).</li><li>Ren, B., Ye, Z., Wang, D., Wu, B., Tan, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+segregation+degree+of+hardening+concrete+using+improved+Mask+R-CNN.">Evaluation of segregation degree of hardening concrete using improved Mask R-CNN.</a> Journal of Hydroelectric Engineering. 41 (5), 93-102 (2022).</li><li>Hu, J., Qin, M., Wang, H., Liu, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Study+on+the+influence+of+vibration+frequency+on+the+performance+of+concrete.">Study on the influence of vibration frequency on the performance of concrete.</a> Highway. 65 (12), 111-114 (2020).</li><li>Bian, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experimental+study+on+characterization+of+evaluation+indexes+for+vibration+compaction+of+fresh+concrete.">Experimental study on characterization of evaluation indexes for vibration compaction of fresh concrete.</a> Journal of Hydroelectric Engineering. 39 (2), 67-80 (2020).</li><li>Liu, Z., Zhou, M., Bai, J., Mou, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+vibration+time+on+the+strength+and+homogeneity+of+rubber+concrete.">Influence of vibration time on the strength and homogeneity of rubber concrete.</a> Industrial Construction. 42, 509-512 (2012).</li><li>Cheng, Y., Gou, Z., Wang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Testing+Investigation+on+Effects+of+Mixing,+Vibrating+and+Curing+on+Strength+of+High-Performance+Concrete.">Testing Investigation on Effects of Mixing, Vibrating and Curing on Strength of High-Performance Concrete.</a> Journal of Northeastern University (Natural Science). 31 (12), 1790-1793 (2010).</li><li>Zhao, Y., Chen, S., Liu, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+mechanism+of+high-frequency+vibration+on+concrete+antifreeze+and+application+in+construction+of+tunnels+in+cold+regions.">Influence mechanism of high-frequency vibration on concrete antifreeze and application in construction of tunnels in cold regions.</a> Industrial Construction. 44 (5), 101-105 (2014).</li><li>Quan, L., Tian, B., Li, S., He, Z., He, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evolution+characteristics+of+flexural+fatigue+performance+of+dense+concrete+consolidated+with+high+frequency+vibration+applied+in+airport+pavement.">Evolution characteristics of flexural fatigue performance of dense concrete consolidated with high frequency vibration applied in airport pavement.</a> Journal of Traffic and Transportation Engineering. 20 (2), 34-45 (2020).</li><li>Cai, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+coarse+aggregate+settlement+induced+by+vibration+on+long-term+chloride+transport+in+concrete:+a+numerical+study.">Influence of coarse aggregate settlement induced by vibration on long-term chloride transport in concrete: a numerical study.</a> Materials and Structures. 55 (9), 1-18 (2022).</li><li>Cai, Y., Liu, Q. F., Yu, L., Meng, Z., Avija, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+experimental+and+numerical+investigation+of+coarse+aggregate+settlement+in+fresh+concrete+under+vibration.">An experimental and numerical investigation of coarse aggregate settlement in fresh concrete under vibration.</a> Cement and Concrete Composites. 122 (7), 104153 (2021).</li><li>Wang, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+application+of+concrete+vibrating+robot+system+for+high+arch+dam.">Development and application of concrete vibrating robot system for high arch dam.</a> Journal of Hydraulic Engineering. 53 (6), 631-654 (2022).</li><li>Chen, C., Li, X., Qiu, Z., Yao, W., Zhu, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Research+Progress+of+Construction+Robots.">Research Progress of Construction Robots.</a> Journal of Architecture and Civil Engineering. 39 (4), 58-70 (2022).</li><li>Shen, H., Zhang, W., Liu, J., Qiu, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+Prospect+of+Construction+Robots+for+High+Rise+Buildings.">Development and Prospect of Construction Robots for High Rise Buildings.</a> Construction Technology. 46 (8), 105-108 (2017).</li><li>Dalal, N., Triggs, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Histograms+of+Oriented+Gradients+for+Human+Detection.">Histograms of Oriented Gradients for Human Detection.</a> IEEE Computer Society Conference on Computer Vision &amp; Pattern Recognition. , (2005).</li><li>Zhou, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gradient-based+Feature+Extraction+From+Raw+Bayer+Pattern+Images.">Gradient-based Feature Extraction From Raw Bayer Pattern Images.</a> IEEE Transactions on Image Processing. (99), 1 (2021).</li><li>He, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Railway+Fastener+Defects+Detection+under+Various+Illumination+Conditions+using+Fuzzy+C-Means+Part+Model.">Railway Fastener Defects Detection under Various Illumination Conditions using Fuzzy C-Means Part Model.</a> Transportation Research Record. 2675 (4), 271-280 (2021).</li><li>Xu, X., Quan, C., Ren, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Facial+expression+recognition+based+on+Gabor+Wavelet+transform+and+Histogram+of+Oriented+Gradients.">Facial expression recognition based on Gabor Wavelet transform and Histogram of Oriented Gradients.</a> IEEE International Conference on Mechatronics &amp; Automation. , (2015).</li><li>Cortes, C., Vapnik, V. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Support+Vector+Networks.">Support Vector Networks.</a> Machine Learning. 20 (3), 273-297 (1995).</li><li>Burges, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Tutorial+on+Support+Vector+Machines+for+Pattern+Recognition.">A Tutorial on Support Vector Machines for Pattern Recognition.</a> Data Mining and Knowledge Discovery. 2 (2), 121-167 (1998).</li><li>Yang, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+Pleurotus+Ostreatus+From+Different+Producing+Areas+Based+on+Mid-Infrared+Spectroscopy+and+Machine+Learning.">Identification of Pleurotus Ostreatus From Different Producing Areas Based on Mid-Infrared Spectroscopy and Machine Learning.</a> Spectroscopy and Spectral Analysis. 43 (2), 577-582 (2023).</li><li>Chaabane, S. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Face+recognition+based+on+statistical+features+and+SVM+classifier).">Face recognition based on statistical features and SVM classifier).</a> Multimedia Tools and Applications. 81 (6), 8767-8784 (2022).</li><li>Saleh, M. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experiments+with+SVM+to+classify+opinions+in+different+domains.">Experiments with SVM to classify opinions in different domains.</a> Expert Systems with Applications. 38 (12), 14799-14804 (2011).</li><li>Zhang, Y., Liu, Y., Wang, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reliability+Prediction+of+Coal+Mine+Water+Disasters+Emergency+Rescue+System+Based+on+Improved+SVM.">Reliability Prediction of Coal Mine Water Disasters Emergency Rescue System Based on Improved SVM.</a> Journal of Zhengzhou University (Engineering Science). 36 (3), 115-119 (2015).</li><li>Cao, Y., Song, D., Hu, X., Sun, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fault+Diagnosis+of+Railway+Point+Machine+Based+on+Improved+Time-Domain+Multiscale+Dispersion+Entropy+and+Support+Vector+Machine.">Fault Diagnosis of Railway Point Machine Based on Improved Time-Domain Multiscale Dispersion Entropy and Support Vector Machine.</a> Acta Electronica Sinica. 51 (1), 117-127 (2023).</li><li>Li, Q., Yuze, C., Yu, B., Ning, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Two-stage+support+vector+machine+method+for+failure+mode+classification+of+reinforced+concrete+columns.">Two-stage support vector machine method for failure mode classification of reinforced concrete columns.</a> Engineering Mechanics. 39 (2), 148-158 (2022).</li><li>Yang, Y., Zhang, W., Yu, H., Chai, W., Liu, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+on+the+relationships+between+ultrasonic+parameters+and+the+stress+state+in+loaded+concrete+based+on+improved+support+vector+machines).">Analysis on the relationships between ultrasonic parameters and the stress state in loaded concrete based on improved support vector machines).</a> Journal of Vibration and Shock. 42 (2), 175-224 (2023).</li><li>Yan, K., Shi, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+elastic+modulus+of+normal+and+high+strength+concrete+by+support+vector+machine.">Prediction of elastic modulus of normal and high strength concrete by support vector machine.</a> Construction &amp; Building Materials. 24 (8), 1479-1485 (2010).</li><li>Hussein, I. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fully-automatic+identification+of+gynaecological+abnormality+using+a+new+adaptive+frequency+filter+and+histogram+of+oriented+gradients+(HOG).">Fully-automatic identification of gynaecological abnormality using a new adaptive frequency filter and histogram of oriented gradients (HOG).</a> Expert Systems. 39 (3), 12789 (2022).</li><li>Chandrakala, M., Devi, P. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Two-stage+classifier+for+face+recognition+using+HOG+features.">Two-stage classifier for face recognition using HOG features.</a> Materials Today: Proceedings. 47, 5771-5775 (2021).</li><li>Long, C., Yichi, Z., Zhangkai, L., Dandan, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Low-Light+Image+Enhancement+Based+on+RAW+Domain+Image.">Low-Light Image Enhancement Based on RAW Domain Image.</a> Journal of Computer-Aided Design &amp; Computer Graphics. 35 (2), 303-311 (2023).</li><li>Wang, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+application+of+concrete+vibrating+robot+system+for+high+arch+dam.">Development and application of concrete vibrating robot system for high arch dam.</a> Journal of Hydraulic Engineering. 53 (6), 631-654 (2022).</li></ol>

Dette papir bruger støttevektormaskinen (SVM) til at lære billedfunktionerne i forskellige konkrete vibrationstilstandsprøver. Baseret på maskinlæringsresultaterne foreslås en konkret metode til genkendelse af vibrationstilstand baseret på billedgenkendelse. For at forbedre genkendelsesnøjagtigheden er det afgørende at kontrollere parametrene for de tre nøgletrin: billedsegmentering, billedbinarisering og retningsbestemt gradientegenværdiekstraktion. Ifølge testresultaterne anvendes en mindre binariseringstæ...

Beton er et grundlæggende byggemateriale, der i vid udstrækning anvendes i byggebranchen. Under pumpning udvikler betonen ofte hulrum, der kræver komprimering gennem vibrationer. Utilstrækkelig vibration kan resultere i en honningkæmmet betonoverflade, mens overdreven vibration kan føre til betonadskillelse 1,2. Kvaliteten af vibrationsdrift påvirker signifikant styrken 3,4,5,6 og holdbarheden af de formede betonkonstruktioner 7,8. Cai et al.9,10 gennemførte en undersøgelse, der kombinerede eksperimentel forskning med numerisk analyse for at undersøge vibrationsmekanismens indflydelsesmekanisme på samlet bundfældning og betonholdbarhed. Resultaterne afslørede, at vibrationstid og aggregerede partikler har en betydelig indvirkning på den samlede bundfældning, mens aggregatdensiteten og plastviskositeten af det cementbaserede materiale har minimale virkninger. Vibrationer forårsager samlet aflejring i bunden af betonprøverne. Når vibrationstiden øges, falder kloridionkoncentrationen desuden i bunden af betonprøverne, mens den stiger markant ved de øverste 9,10.
I øjeblikket er vurderingen af betonvibrationstilstand overvejende afhængig af manuel vurdering. Da byggebranchen fortsætter med at udvikle sig gennem intelligente reformer, er robotoperationer opstået som den fremtidige retning 11,12. Derfor er en afgørende udfordring i intelligente vibrationsoperationer, hvordan robotter kan identificere betonens vibrationstilstand.
Histogrammet for den orienterede gradient er en teknik, der bruger intensitetsgradienten af pixels eller fordelingen af kantretninger som en deskriptor til at karakterisere repræsentationen og formen af objekter i billeder13,14. Denne fremgangsmåde fungerer på billedets lokale gitterceller, hvilket giver robust stabilitet til karakterisering af billedændringer under forskellige geometriske og optiske forhold.
Zhou et al.15 foreslog en metode til direkte udtrækning af retningsgradientfunktioner fra Bayer-tilstandsbilleder. Denne fremgangsmåde udelader adskillige trin i beregningen af retningsbestemt forløb ved at matche farvefilterkolonnen med gradientoperatoren, hvilket reducerer beregningskravene til retningsbestemt gradientbilledgenkendelse betydeligt. Han et al.16 udnyttede retningsgradienthistogrammet som det underliggende træk og anvendte den gennemsnitlige klyngealgoritme til at klassificere skinnefastgørelseselementer og bestemme, om fastgørelseselementerne er defekte. Genkendelsesresultaterne viste, at histogrammet for den orienterede gradientfunktion udviste høj følsomhed over for fastgørelsesfejl, hvilket opfyldte behovene for jernbanevedligeholdelse og reparation. I en anden undersøgelse forbehandlede Xu et al.17 ansigtsbilledfunktioner ved hjælp af Gabor-bølgeletfiltrering og reducerede dimensionen af funktionsvektorer gennem binær kodning og HOG-algoritmen. Den gennemsnitlige genkendelsesnøjagtighed af metoden er 92,5%.
Støttevektormaskinen (SVM)18 bruges til at kortlægge vektoren i et højdimensionelt rum og etablerer et adskillende hyperplan med en passende retning for at maksimere afstanden mellem to parallelle hyperplaner. Dette giver mulighed for klassificering af støttevektorer19. Forskere har forbedret og optimeret denne klassificeringsteknologi, hvilket fører til dens anvendelse på forskellige områder såsom billedgenkendelse20,21, tekstklassificering22, pålidelighedsforudsigelse23 og fejldiagnose24.
Li et al.25 udviklede en to-trins SVM-model til seismisk fejlmønstergenkendelse med fokus på tre seismiske fejltilstande. Analyseresultaterne viser, at den foreslåede totrins SVM-metode kan opnå mere end 90 % nøjagtighed for de tre fejltilstande. Yang et al.26 integrerede en optimeringsalgoritme med SVM for at simulere forholdet mellem de fem ultralydsparametre og belastningen af den belastede beton. Ydeevnen for en uoptimeret SVM er utilfredsstillende, især i lavstressfasen. Imidlertid giver gennemgang af modellen optimeret af algoritmen forbedrede resultater, omend med lange beregningstider. Til sammenligning reducerer den partikelsværmoptimeringsoptimerede SVM beregningstiden betydeligt, samtidig med at den leverer optimale simuleringsresultater. Yan et al.27 anvendte SVM-teknologi og introducerede en præcisionsufølsom tabsfunktion til at forudsige det elastiske modul af højstyrkebeton og sammenligne dens forudsigelsesnøjagtighed med den traditionelle regressionsmodel og neurale netværksmodel. Forskningsresultaterne viser, at SVM-teknologien producerer en mindre forudsigelsesfejl for elastisk modul sammenlignet med andre metoder.
Dette papir indsamler billedprøver af beton under forskellige vibrationstilstande og beskriver betonens forskellige tilstande ved hjælp af retningsgradienthistogramteknikken. Retningsgradienten anvendes som en funktionsvektor til træning af SVM, og undersøgelsen fokuserer på levedygtigheden af at bruge retningsgradienthistogram-SVM-teknologien til at identificere betonens vibrationstilstand. Derudover analyserer papiret indflydelsesmekanismen mellem tre nøgleparametre - binariseringstærskel, retningsbestemt gradientstatistisk blokstørrelse og retningsgradientstatistisk intervalnummer - i funktionsekstraktionsprocessen for retningsgradienthistogrammet og genkendelsesnøjagtigheden af SVM.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>camera</td>
			<td>SONY</td>
			<td>A6000</td>
			<td>The sensor size is 23.5x15.6mm, the maximum acquisition resolution is 1440 * 1080, and the effective pixel is 24.3 million.</td>
		</tr>
		<tr>
			<td>concrete</td>
			<td>Wuhan Construction Changxin Technology Development Co., Ltd.</td>
			<td>C30 pumping concrete</td>
			<td>According to the standard of &#39; concrete strength test and evaluation standard &#39; ( GB / T 50107-2010 ), the standard value of cubic compressive strength is 30 MPa pumping concrete.</td>
		</tr>
		<tr>
			<td>Matlab</td>
			<td>MathWorks</td>
			<td>Matlab R2017a</td>
			<td>MATLAB&#39;s programming interface provides development tools for improving code quality maintainability and maximizing performance. 
			It provides tools for building applications using custom graphical interfaces. 
			It provides tools for combining MATLAB-based algorithms with external applications and languages</td>
		</tr>
		<tr>
			<td>Processor&#160;</td>
			<td>Intel</td>
			<td>12th Gen Intel(R) Core (TM) i7-12700H @ 2.30GHz</td>
			<td>64-bit Win11 processor&#160;</td>
		</tr>
	</tbody>
</table>

image recognition and parameter analysis of concrete vibration state based on support vector machine

I dette papir anvendes retningsgradienthistogramteknologien til at udtrække funktionerne i konkrete billedprøver taget under forskellige vibrationstilstande. Understøttelsesvektormaskinen (SVM) bruges til at lære forholdet mellem billedfunktioner og vibrationstilstand. Maskinlæringsresultaterne bruges efterfølgende til at vurdere gennemførligheden af den konkrete vibrationstilstand. Samtidig analyseres indflydelsesmekanismen for beregningsparametrene for retningsgradienthistogrammet på genkendelsesnøjagtigheden. Resultaterne demonstrerer muligheden for at bruge retningsgradienthistogram-SVM-teknologien til at identificere betonens vibrationstilstand. Genkendelsesnøjagtigheden øges først og falder derefter, når blokstørrelsen af retningsgradienten eller antallet af statistiske intervaller øges. Genkendelsesnøjagtigheden falder også lineært med stigningen i binariseringstærsklen. Ved at bruge eksempelbilleder med en opløsning på 1024 pixel x 1024 pixels og optimere parametrene for funktionsudtrækning kan der opnås en genkendelsesnøjagtighed på 100 %.

Concrete is a fundamental building material extensively used in the construction industry. During pumping, the concrete frequently develops voids that require compaction through vibration. Inadequate vibration may result in a honeycombed concrete surface, while excessive vibration can lead to concrete segregation1,2. The quality of vibration operation significantly impacts the strength3,4,5,6 and durability of the formed concrete structures7,8. Cai et al.9,10 conducted a study that combined experimental research with numerical analysis to investigate the influence mechanism of vibration on aggregate settlement and concrete durability. The findings revealed that vibration time and aggregate particles exert a substantial impact on aggregate settlement, while aggregate density and the plastic viscosity of the cement-based material have minimal effects. Vibration causes aggregate deposition at the bottom of the concrete specimens. Moreover, as the vibration time increases, the chloride ion concentration decreases at the bottom of the concrete specimens while significantly increasing at the top9,10.
Currently, the assessment of concrete vibration state relies predominantly on manual judgment. As the construction industry continues to progress through intelligent reforms, robot operations have emerged as the future direction11,12. Consequently, a crucial challenge in intelligent vibration operations is how to enable robots to identify the vibration state of concrete.
The histogram of the oriented gradient is a technique that utilizes the intensity gradient of pixels or the distribution of edge directions as a descriptor to characterize the representation and shape of objects in images13,14. This approach operates on the local grid cells of the image, providing robust stability in characterizing image changes under various geometric and optical conditions.
Zhou et al.15 proposed a method for directly extracting directional gradient features from Bayer mode images. This approach omits numerous steps in calculating the directional gradient by matching the color filter column with the gradient operator, thereby significantly reducing the computational requirements for directional gradient image recognition. He et al.16 utilized the directional gradient histogram as the underlying feature and employed the mean clustering algorithm to classify rail fasteners and determine whether the fasteners are defective. The recognition results indicated that the histogram of the oriented gradient feature exhibited high sensitivity to fastener defects, meeting the needs of railway maintenance and repair. In another study, Xu et al.17 preprocessed face image features using Gabor wavelet filtering and reduced the dimension of feature vectors through binary coding and the HOG algorithm. The average recognition accuracy of the method is 92.5%.
The support vector machine (SVM)18 is used to map the vector into a high-dimensional space and establishes a separating hyperplane with a suitable direction to maximize the distance between two parallel hyperplanes. This allows for the classification of support vectors19. Scholars have improved and optimized this classification technology, leading to its application in various fields such as image recognition20,21, text classification22, reliability prediction23, and fault diagnosis24.
Li et al.25 developed a two-stage SVM model for seismic failure pattern recognition, focusing on three seismic failure modes. The analysis results indicate that the proposed two-stage SVM method can achieve more than 90% accuracy for the three failure modes. Yang et al.26 integrated an optimization algorithm with the SVM to simulate the relationship between the five ultrasonic parameters and the stress of the loaded concrete. The performance of an unoptimized SVM is unsatisfactory, particularly in the low-stress stage. However, traversing the model optimized by the algorithm yields improved results, albeit with lengthy computation times. In comparison, the particle swarm optimization optimized SVM significantly reduces the calculation time while delivering optimal simulation results. Yan et al.27 employed SVM technology and introduced a precision-insensitive loss function to predict the elastic modulus of high-strength concrete, comparing its prediction accuracy against the traditional regression model and neural network model. The research findings demonstrate that the SVM technology produces a smaller prediction error for elastic modulus compared to other methods.
This paper collects image samples of concrete under various vibration states and describes the concrete&#39;s different states using the directional gradient histogram technique. The directional gradient is employed as a feature vector for training the SVM, and the study focuses on the viability of using the directional gradient histogram-SVM technology to identify the vibration state of concrete. Additionally, the paper analyzes the influence mechanism between three key parameters-binarization threshold, directional gradient statistical block size, and directional gradient statistical interval number-in the feature extraction process of the directional gradient histogram and the recognition accuracy of the SVM.

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines 

Billedgenkendelse og parameteranalyse af betonvibrationstilstand baseret på støttevektormaskine

Denne protokol har til formål at analysere, hvordan beregningsparametrene for trevektorerne for retningsgradientfunktionen påvirker SVM's nøjagtighed ved identifikation af betonvibrationstilstanden. De primære beregningsparametre for retningsgradientfunktionsvektoren inkluderer den retningsbestemte gradientstatistiske blokstørrelse, antallet af retningsgradientstatistiske vinkelintervaller og den binære grå tærskel. Dette afsnit bruger tre hovedberegningsparametre som variabler til at designe testen. Testparamete...

Watch this Scientific Journal Video about Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines at JoVE.com

Protokollen beskrevet i dette papir anvender retningsgradienthistogramteknikken til at udtrække egenskaberne ved konkrete billedprøver under forskellige vibrationstilstande. Den anvender en supportvektormaskine til maskinindlæring, hvilket resulterer i en billedgenkendelsesmetode med minimale krav til træningsprøver og lave krav til computerens ydeevne.

In this paper, the directional gradient histogram technology is employed to extract the features of concrete image samples captured under different ...

Protokollen beskrevet i dette papir udnyttede retningsgradienthistogramteknikken til at ekstrahere egenskaberne ved et konkret billedprøve under forskellige vibrationsceller. Den anvender en supportvektormaskine til maskinindlæring, hvilket resulterer i billedgenkendelsesmetode med minimale trænede prøvekrav og lave krav til computerens ydeevne. Denne fremgangsmåde reducerer antallet af krævede prøver betydeligt og sænker kravene til computerens ydeevne.Med en bærbar computer, der svarer til 2,3 gigahertz centrale behandlingsenheder, fuldender genkendelsesprocessen differentieringen af togpladsen for den understøttede vektormaskine inden for kun 50 sekunder. Billedsegmenteringen under størrelse på 128 projekter og 128 projekter udnyttes. Antallet af retningsvektorer for statistisk vinkel invers er indstillet til 12.I billedprocessen med 224 opløsninger opnås den bedste genkendelsesforekomst for resultatet af maskinel indlæring.

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Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Mixing, Placing and Curing Concrete 

SCIENTISTS IN ACTION

In this paper, the directional gradient histogram technology is employed to extract the features of concrete image samples captured under different vibration states. The support vector machine (SVM) is utilized to learn the relationship between image features and vibration state. The machine learning results are subsequently used to assess the feasibility of the concrete vibration state. Simultaneously, the influence mechanism of the calculation parameters of the directional gradient histogram on the recognition accuracy is analyzed. The results demonstrate the feasibility of using the directional gradient histogram-SVM technology to identify the vibration state of concrete. The recognition accuracy initially increases and then decreases as the block size of the directional gradient, or the number of statistical intervals increases. The recognition accuracy also decreases linearly with the increase of the binarization threshold. By using sample images with a resolution of 1024 pixels x 1024 pixels and optimizing the feature extraction parameters, a recognition accuracy of 100% can be attained.

The protocol described in this paper utilizes the directional gradient histogram technique to extract the characteristics of concrete image samples under various vibration states. It employs a support vector machine for machine learning, resulting in an image recognition method with minimal training sample requirements and low computer performance demands.

Concrete Sample Image Acquisition to Extract Concrete Characteristics

Indsamling af konkrete prøvebilleder for at udtrække konkrete egenskaber

Gray Binarization of Concrete Image Sample

Grå binarisering af konkret billedprøve

Calculation of Directional Gradient Eigenvalue for Support Vector Machine Training

After binarizing the concrete images, calculate the horizontal and vertical binary gradient of each pixel in the image using this equation. Calculate the binary gradient direction and size of each point using this equation. Then determine the size of the image segmentation block represented as n.To divide the image into square blocks of n by n, set a segmentation line for every n pixels along the horizontal axis and the vertical axis. Classify pixels into the appropriate gradient's statistical angle interval of each direction based on the binary gradient direction of each pixel in the block. To obtain the gradient statistical value of that interval, sum the binary gradient of the pixels in the gradient statistical angle interval of each direction in the counterclockwise direction.Record the results obtained for the gradient statistical angle interval directional gradient statistics. Next, divide samples into precise calculation areas where each area consists of four adjacent blocks. Calculate the statistical value of the directional gradient within the angle interval for each block in the specified calculation area.Then generate the feature vector with the directional gradient's statistics as a component. Combine the directional gradient feature vectors derived from each calculation area to obtain the directional gradient feature vector of the image. As the block size is expanded to a certain extent, the directional gradient features in each block of concrete image samples with different vibration states exhibit significant differences.